strojové učení / машинне навчання (Python)

import numpy as np

from keras.models import Sequential

from keras.layers import Dense

import matplotlib.pyplot as plt

x = np.linspace(-5, 5, 1000)

y = x**2

model = Sequential()

model.add(Dense(10, input_dim = 1, activation= "relu"))

model.add(Dense(1))

model.compile(loss = "mse" , optimizer="adam")

model.fit(x, y, epochs = 10000, verbose =0)

<keras.callbacks.History at 0x7f077d1028f0> 

plt.plot(x, y, label = "ground truth")

plt.plot(x, model.predict(x), label = "predicted")

32/32 [==============================] - 0s 1ms/step

[<matplotlib.lines.Line2D at 0x7f077d886320>] 

x_new = np.array([-2, -1, 0, 1, 2 ])

y_real1 = x_new ** 2

x_new = x_new.reshape( -1, 1)

y_pred = model.predict(x_new)

for i in range(x_new)):

  print(f"{x_new[i][0]:.2f}  ")